1. Technical Field
The present invention generally relates to wireless communication receivers, such as those used in cellular and other wireless communication networks, and particularly relates to determining interference characteristics for a dominant interferer signal according to a computationally efficient model.
2. Background
“Rake” receivers, which are well known in the art of receiver design for wireless communications, exploit multipath reception for improved signal to noise ratios. In operation, each of two or more Rake fingers obtains despread values from a received CDMA (code division multiple access) signal by correlating the received signal against a known spreading sequence. By aligning the processing delay of each finger with a different path delay of the multipath signal, the Rake receiver effectively obtains a different copy of the desired signal for each delay to which a finger is assigned. Maximum ratio combining of the finger signals yields, at least in theory, a combined signal having improved signal to noise ratio as compared to the signal from any one finger.
The above “standard” Rake operation in fact works well in white noise environments, where the signal impairments, including incident interference, are uncorrelated across the fingers. Standard Rake performance becomes decidedly sub-optimum in colored noise environments, where at least some components of the overall received signal impairments may be strongly correlated across the fingers. In other words, the standard Rake receiver does not perform well in terms of suppressing colored interference, where the received signal impairments across Rake fingers may exhibit significant correlations.
One efficient linear receiver structure that does achieve potentially very good suppression of colored interference is the “Generalized RAKE” (GRake) receiver. The GRake receiver implements an LMMSE solution that both reduces the effect of the dispersive channel (partially restores the lost orthogonality between the spreading codes from the desired base station) and whitens the interfering signal from other sources (neighboring cells, other systems, receiver filtering). This is achieved by accounting for the covariance of received signal impairments between the Rake fingers, where the tasks of own-cell and other interference suppression are combined optimally to achieve the largest possible reduction in the impairment signal power.
A basic presentation for one embodiment of GRake receiver operation appears in Bottomley, et al., Generalized Rake Reception for Cancelling Interference from Multiple Base Stations,” IEEE Vehicular Technology Conference (2000). One may also refer to U.S. Pat. No. 6,363,104 B1 to Bottomley et al., for GRake details. Those skilled in the art will appreciate that an equivalent framework of operations may be carried out in a LMMSE (Linear Minimum Means Square Error) chip equalizer (CE), wherein receiver performance in colored noise environments is improved by considering impairment correlations between the equalization filter tap delays. The optimal weight CE solution is equal to that of GRake within a scalar multiplication.
In any case, in dispersive environments, where the colored component of interference dominates, the GRake receiver, chip equalizer, or other type of “linear equalization” receiver, may increase the SIR after (signal) combining by several dB on average. Of course, the best interference suppression is achieved if up-to-date covariance information (the “instantaneous color” of the impairment component) is used when determining the combining weights. One may refer to U.S. Pat. No. 6,714,585 B1 to Wang et al. for information related to combining weight generation that consider received signal impairment correlations.
Rather than directly calculating received signal impairment correlations, it is known to represent received signal impairments according to a parametric model that is dynamically “fitted” to ongoing observations of impairment, which may be short-term, somewhat “noisy” snapshots of received signal impairment. In more detail, in the parametric GRake receiver, an overall received signal covariance matrix is constructed based on available channel information and is expressed as the combination of various constituent components of impairment. The relative weights (fitting parameters) of these components are determined dynamically, such as by fitting the model terms to ongoing impairment correlation measurements. One may refer to U.S. Published Application 2005/0201447 A1 to Cairns et al. for examples of parametric model-based impairment correlation processing.
As a key aspect, the parametric model based approach only accounts for the components of received signal impairment correlation that it explicitly models. If a significant fraction of the interference experienced by a given parametric-model based receiver comes from an un-modeled source of interference, then receiver performance can suffer significantly. For example, if a significant portion of interference at the receiver comes from a neighboring cell, then, in order to suppress it, the receiver needs a representation for the other cell interference in its parametric model. This approach is illustrated, for example, in the co-pending and commonly assigned patent application, entitled, “Method and Apparatus for Extended Least Squares Estimation for Generalized Rake Receiver Parameters Using Multiple Base Stations,” filed on 12 Dec. 2006, assigned Ser. No. 11/609,373, and now published as U.S. 2007/0098048 A1. In addition to the own cell interference, that application teaches fully modeling the interference arising from a neighboring cell using the same multipath signal structure.
While the performance gains associated with including additional parametric model terms may be significant, there is an unavoidable, potentially significant increase in computational complexity. As such, the desire or need to suppress multiple sources of interference giving rise to correlated impairments in a linear equalization environment must be balanced against the need to keep the computational complexity within the bounds that are reasonable for the signal processing (time and/or computational capability) and power consumption constraints at play in a given application.